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9,152 results for “Networking”
Dataset - seismic data from central-western Italy used in the paper on rapid prediction of ground motion using a Convolutional Neural Network
<p>The dataset published here is the central-western Italy dataset used in the paper "<em>Transfer learning: Improving neural network based prediction of earthquake ground shaking for an area with insufficient training data"</em> (<a href="https://arxiv.org/abs/2105.05075">https://arxiv.org/abs/2105.05075</a>). The code for the paper is available at <a href="https://github.com/djozinovi/TLpredIM">https://github.com/djozinovi/TLpredIM</a>. The abstract of the paper:</p> <blockquote> <p>In a recent study (Jozinović et al, 2020) we showed that convolutional neural networks (CNNs) applied to network seismic traces can be used for rapid prediction of earthquake peak ground motion intensity measures (IMs) at distant stations using only recordings from stations near the epicenter. The predictions are made without any previous knowledge concerning the earthquake location and magnitude. This approach differs from the standard procedure adopted by earthquake early warning systems (EEWSs) that rely on location and magnitude information. In the previous study, we used 10 s, raw, multistation waveforms for the 2016 earthquake sequence in central Italy for 915 events (CI dataset). The CI dataset has a large number of spatially concentrated earthquakes and a dense station network. In this work, we applied the CNN model to an area around area near Pisa, Italy. In our initial application of the technique, we used a dataset consisting of 266 earthquakes recorded by 39 stations. We found that the CNN model trained using this smaller dataset performed worse compared to the results presented in the original study by Jozinović et al. (2020). To counter the lack of data, we adopted transfer learning (TL) using two approaches: first, by using a pre-trained model built on the CI dataset and, next, by using a pre-trained model built on a different (seismological) problem that has a larger dataset available for training. We show that the use of TL improves the results in terms of outliers, bias, and variability of the residuals between predicted and true IMs values. We also demonstrate that adding knowledge of station positions as an additional layer in the neural network improves the results. The possible use for EEW is demonstrated by the times for the warnings that would be received at the station PII.</p> </blockquote>
In materia reservoir computing with a fully memristive architecture based on self-organizing nanowire networks - Dataset
<p>This is the dataset of "<em>In materia</em> reservoir computing with a fully memristive architecture based on self-organizing nanowire networks"</p>
Mental health network of Gipuzkoa dataset (2015) for benchmark analysis
<p>This dataset contains data from the Mental Health System of Gipuzkoa, which are described in the manuscript: "Garcia-Alonso, CR., Almeda, N., Salinas-Pérez, JA., Gutiérrez-Colosía, MR., Iruin-Sanz, A., & Salvador-Carulla., L. (2021). Use of a decision support system for benchmarking analysis and organizational improvement of regional mental health care: The case of Gipuzkoa (Basque Country, Spain)". This manuscript has been submitted to Plos One journal.</p> <p>This research focused on developing an analytical process for assessing the performance of the Mental health (MH) system of Gipuzkoa and identifying benchmark and target-for-improvement catchment areas. For doing so a decision support system that integrated data envelopment analysis, Monte Carlo simulation and artificial intelligence was used. The units of analysis, which are considered the decision-making units, were the 13 catchment areas defined by a reference MH centre. The following indicators were assessed: relative technical efficiency, stability and entropy to guide organizational interventions.</p> <p>Main results of the analyses pointed out that the MH system of Gipuzkoa showed high efficiency scores in each main type of care (inpatient, day and outpatient), but it can be considered unstable (small changes can have relevant impacts on MH provision and performance). With regards to performance improvement, it is recommended to reduce admissions and readmissions for inpatient care, increase workforce capacity and utilization of day care services and increase the availability of outpatient care services.</p>
Trained network for segmentation of yeast cell from brightfield images in microfluidic traps - DetecDiv (id02)
<pre>Trained network for segmentation of yeast cell from brightfield images in microfluidic traps. Related to the dataset: <a href="https://doi.org/10.5281/zenodo.5553771">https://doi.org/10.5281/zenodo.5553771</a></pre> <p><strong>------------------------------------------</strong></p> <p><strong>Author(s)</strong>: Théo, ASPERT</p> <p><strong>Contact email</strong>: theo.aspert@gmail.com</p> <p><strong>Affiliation</strong>: IGBMC, Université de Strasbourg</p> <p><strong>Funding bodies</strong>: This work was supported by the Agence Nationale pour la Recherche, the grant ANR-10-LABX-0030-INRT, a French State fund managed by the Agence Nationale de la Recherche under the frame program Investissements d'Avenir ANR-10-IDEX-0002-02.</p>
Trained network for cell-cycle slowdown detection - DetecDiv (id03)
<pre>Trained network for cell-cycle slowdown detection. Related to the dataset: <a href="https://doi.org/10.5281/zenodo.5553796">doi.org/10.5281/zenodo.5553796</a></pre> <p><strong>------------------------------------------</strong></p> <p><strong>Author(s)</strong>: Théo, ASPERT</p> <p><strong>Contact email</strong>: theo.aspert@gmail.com</p> <p><strong>Affiliation</strong>: IGBMC, Université de Strasbourg</p> <p><strong>Funding bodies</strong>: This work was supported by the Agence Nationale pour la Recherche, the grant ANR-10-LABX-0030-INRT, a French State fund managed by the Agence Nationale de la Recherche under the frame program Investissements d'Avenir ANR-10-IDEX-0002-02.</p>
Trained network for classification of images from yeast cell lifespans - DetecDiv (id01)
<p>Trained network for classification of images from yeast cell lifespans.</p> <p>Related to the dataset: <a href="https://doi.org/10.5281/zenodo.5552642">doi.org/10.5281/zenodo.5552642</a></p> <p>Testset: <a href="https://doi.org/10.5281/zenodo.5866747">doi.org/10.5281/zenodo.5866747 </a></p> <p><strong>------------------------------------------</strong></p> <p><strong>Author(s)</strong>: Théo, ASPERT</p> <p><strong>Contact email</strong>: theo.aspert@gmail.com</p> <p><strong>Affiliation</strong>: IGBMC, Université de Strasbourg</p> <p><strong>Funding bodies</strong>: This work was supported by the Agence Nationale pour la Recherche, the grant ANR-10-LABX-0030-INRT, a French State fund managed by the Agence Nationale de la Recherche under the frame program Investissements d'Avenir ANR-10-IDEX-0002-02.</p>
[Re] An anatomically constrained neural network model of fear conditioning
<p>The results contained within this archive correspond to the Python re-implementation of the computation model and replication of the classical conditioning experiment described in Armony et al. (1995). The data was generated by running the program using the 14th frequency as the Conditioned Stimulus (CS_IDX = 13) and setting the random seed for the <a href="https://numpy.org/">Numpy</a> library to 3 (NUMPY_SEED = 3).<br> During the pre- and post-conditioning testing phases, the activation values of all the neurons in the model have been recorded in different <a href="https://pandas.pydata.org/">pandas.DataFrames</a>. At the end of the experiment, those DataFrames have been written to disk using the <a href="https://hdfgroup.org/">HDF5</a> file format. It should be noted that although this might have been unnecessary given the size of the final dataset, the file has been further compressed to save on space.</p> <p>The HDF5 file format works similarly to dictionaries in Python, or Maps in other programming languages. That is, the data is organized into tables/arrays each associated with a unique key. In the case of the current dataset, the keys are the name of the different layers in lowercase (i.e.: mgm, mgv, cortex, and amygdala). Then, the array corresponding to each of those key includes the layer's neural activities for all frequencies, and for both the pre- and post-conditioning phases.<br> The columns making up each table are:</p> <ul> <li>The "Frequency" index with values in the range [1-15],</li> <li>One column for storing the activity of each unit ("Unit 1", ..., "Unit N", where N = 3 or N = 8 depending on the layer),</li> <li>The last column, entitled "Phase", contains string representations of the phase during which the activity was recorded (either "Pre-conditioning" or "Post-conditioning").</li> </ul> <p>The data included in the archive can be retrieved and stored in a dictionary for further processing using the following Python script:</p> <pre><code class="language-python">import pandas as pd # DATA_PATH is the absolute path to the file containing the hdf5 formated data data = {k: pd.read_hdf(DATA_PATH, key=k) for k in ['mgm', 'mgv', 'cortex', 'amygdala']}</code></pre> <p> </p>
Data of "Interaction-based material network: a general framework for (porous) microstructured materials"
<pre>Data related to the publication (we would be grateful if you could cite the paper in the case in which you are using the data) title = "Interaction-based material network: a general framework for (porous) microstructured materials", journal = "Computer Methods in Applied Mechanics and Engineering", pages = " ", year = "202?", issn = "0045-7825", doi = "https://doi.org/10.1016/j.cma.", author = "Nguyen, Van Dung and Noels, Ludovic"</pre>
Network analysis reveals rare disease signatures across multiple levels of biological organization - Co-expression dataset
<p>The GTEx-derived co-expression data in 38 tissues generated in Buphamalai et.al., Network analysis reveals rare disease signatures across multiple levels of biological organization, Nature Communications 2021. Please see the publication's Methods section for details.</p>
The NGI Forward semantic social network data
<p>The <a href="https://research.ngi.eu/">NGI Forward project</a> is part of the European Union's <a href="https://www.ngi.eu">Next Generation Internet Initiative</a>. It is meant to provide European institutions with policy advice for how to shape the future, human-centric Internet. As part of it, a team of ethnographers coded a specially convened online conversation, then arranged its results into a semantic social network. This dataset encodes that conversation, as well as the results of the coding exercise, in raw data form for further exploration and replication purposes. The dataset is pseudonymized.</p> <ul> <li><a href="https://exchange.ngi.eu/">Funnel website</a> of the project.</li> <li><a href="https://journals.sagepub.com/doi/10.1177/1525822X20908236">About semantic social networks</a>.</li> <li><a href="https://edgeryders.eu/t/long-term-ssna-data-storage-documentation-manual/12786">Data export and documentation process</a> (contains links to the code used to export the data)</li> </ul>
Database of the Italian disdrometer network (V04)
<p><span lang="EN-US">This is the Version 04 (V04) of the GID database that includes the data collected by the GID disdrometer network along the Italian peninsula. The main upgrade of V04 with respect to the V03 is the higher number of disdrometer data due to the presence of new sensors in the GID network (in the V03 there were 19 disdrometers, while in the V04 there are 23 disdrometers) and the availability of more years of measurements (i.e. with respect to V03 we added data until 31 December 2024). <span> </span></span></p> <p><span lang="EN-US">The disdrometers belong to several Italian institutions that are part of the Italian Group of Disdrometry (in Italian it reads: Gruppo Italiano Disdrometria, GID, </span><span><a href="https://www.gid-net.it/"><span lang="EN-US">https://www.gid-net.it/</span></a></span><span lang="EN-US">). </span></p> <p><span lang="EN-US">In particular, the GID database contains the 1-minute Drop Size Distributions (DSD) obtained from the data of the laser disdrometers of the GID network during rainy minutes. For each disdrometer, data are available up to 31 December 2024. The database is structured in 23 sub-folders (one for each disdrometer), the name of these subfolders is the disdrometer ID in five digits (for more information see </span><span><a href="https://www.gid-net.it/network/"><span lang="EN-US">https://www.gid-net.it/network/</span></a></span><span lang="EN-US">). In each of these folders, there is one .xlsx file for each year of measurement. The latter file reports the time and the DSDs collected by the selected disdrometer during a given year. Following there are the mean value of class diameter (in mm) and the diameter class width (in mm) used to compute the DSD for the two types of laser disdrometers available in the GID network: </span></p> <p><span lang="EN-US"><span>-<span> </span></span></span><span lang="EN-US">Thies Clima Laser Precipitation Monitor disdrometer (TC):</span></p> <p><span lang="EN-US"><span>·<span> </span></span></span><span lang="EN-US">Mid-value of class diameter (mm): [0.1875, 0.3125, 0.4375, 0.625, 0.875, 1.125, 1.375, 1.625, 1.875, 2.25, 2.75, 3.25, 3.75, <span> </span>4.25, 4.75, 5.25, 5.75, 6.25, 6.75, 7.25, 7.75, 9];</span></p> <p><span lang="EN-US"><span>·<span> </span></span></span><span lang="EN-US">Diameter class width (mm): [0.125, 0.125, 0.125, 0.250, 0.250, 0.250, 0.250, 0.250, 0.250, 0.500, 0.500, 0.500, 0.500, 0.500, <span> </span>0.500, 0.500, 0.500, 0.500, 0.500, 0.500, 0.500, 1.25];</span></p> <p><span lang="EN-US"><span>-<span> </span></span></span><span lang="EN-US">OTT Parsivel 2 disdrometer (P2)</span></p> <p><span lang="EN-US"><span>·<span> </span></span></span><span lang="EN-US">Mid-value of class diameter (mm): [0.062, 0.187, 0.312, 0.437, 0.562, 0.687, 0.812,0.937, 1.062, 1.187, 1.375, 1.625, 1.875, 2.125,2.375, 2.750, 3.250, 3.750, 4.250, 4.750, 5.500,6.500, 7.500, 8.500, 9.500, 11.00, 13.00, 15.00,17.00, 19.00, 21.50, 24.50];</span></p> <p><span lang="EN-US"><span>·<span> </span></span></span><span lang="EN-US">Diameter class width (mm): [0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125,0.125, 0.125, 0.125, 0.250, 0.250, 0.250, 0.250,0.250, 0.500, 0.500, 0.500, 0.500, 0.500, 1.000,1.000, 1.000, 1.000, 1.000, 2.000, 2.000, 2.000,2.000, 2.000, 3.000, 3.000];</span></p> <p><span lang="EN-US">To obtain uniform and high-quality DSD, the same processing has been adopted to all the disdrometer raw data (i.e. the V02 of the GID processing). The description of V02 GID processing, along with more information on the GID database structure are available in the data paper "Database of the Italian disdrometer network" published on June 2023 in the Journal "Earth System Science Data" (DOI: </span><span><a href="https://doi.org/10.5194/essd-15-2417-2023"><span lang="EN-US">https://doi.org/10.5194/essd-15-2417-2023</span></a></span><span lang="EN-US">). For further information contact the GID team at <strong>gid.info@gid-net.it</strong>.</span></p>
Replication materials for Schmutte (2014) "Free to Move? A Network Approach to the Analysis of Job Mobility"
<p>This folder contains datasets and coded needed to replicate results published in Schmutte, Ian M. (2014) "Free to Move? A Network Approach to the Analysis of Job Mobility, " Labour Economics vol.29 pp.49--61</p>
Evolutionary history and precipitation seasonality shape niche overlap in Neotropical bat-plant pollination networks
<p>Species interactions are one dimension of the niche, and niche overlap arises when two species share an interaction partner. In pollination systems, environmental and biotic factors impact the niche overlap. Here we explored the effects of climate seasonality, plant and bat richness, morphological traits, and phylogenetic distance in shaping the niche overlap of Neotropical bat-plant pollination networks. For that, we used a dataset of 22 bat-plant pollination networks in the Neotropical region. We measured niche overlap in bats and plants with the Morisita-Horn index, ĈH, and then, we used a SAR model to test the relationships between niche overlap and the abiotic and biotic factors. We found a lower niche overlap among bats in communities composed of phylogenetically distant bat species. Moreover, plant and bat overlap were lower in regions with higher precipitation seasonality. Our results indicate that climate seasonality and bat evolutionary history drive niche overlap in Neotropical bat-plant pollination interactions. These findings suggest that a higher precipitation seasonality may promote the emergence of temporal modules reducing niche overlap, probably as a consequence of seasonal species phenologies. Furthermore, the method used to record the interactions impacts the degree of niche overlap. Interactions recorded with pollen samples tend to have higher niche overlap than direct observations. The uncoupled responses of morphological traits and phylogenetic distances in bat niche overlap suggest an effect of historical processes independently of the morphological traits. Our study reinforced the importance of evolutionary history and ecological processes in imprinting patterns of interaction niche overlap.</p>
Dataset for: Synthetic Micrographs of Bacteria (SyMBac) Allows Accurate Segmentation of Bacterial Cells Using Deep Neural Networks
<p>Datasets for the paper Synthetic Micrographs of Bacteria (SyMBac) Allows Accurate Segmentation of Bacterial Cells Using Deep Neural Networks, published in BMC Biology.</p>
Risk of bias assessments and support for judgement with ROB 2 tool for the Cochrane Review: PEG-asparaginase treatment for acute lymphoblastic leukaemia in children: a network meta-analysis
<p>Risk of bias assessments and support for judgement with ROB 2 tool for the Cochrane Review: PEG-asparaginase treatment for acute lymphoblastic leukaemia in children: a network meta-analysis</p>
Critical transitions in degree mixed networks: A discovery of forbidden tipping regions in networked spin systems
<p>Simulation data of critical transitions obtained by running a networked implementation of the Ising model on networks with different degree sequences. </p>
Dataset for "Invariance of Object Detection in Untrained Deep Neural Networks"
<p><strong>Dataset for<br> "Invariance of Object Detection in Untrained Deep Neural Networks"</strong><br> Jeonghwan Cheon, Seungdae Baek, and Se-Bum Paik*<br> *Contact: sbpaik@kaist.ac.kr<br> <br> To run demo codes for "<a href="https://github.com/vsnnlab/Invariance">Invariance of Object Detection in Untrained Deep Neural Networks</a>", please download files below.<br> <br> <strong>1. Image.zip</strong><br> <strong>- Object dataset (Foldername: selectivity_var)</strong>: This set was used to find units that selectively respond to a specific object class. It contains nine object classes (bed, chair, desk, dresser, nightstand, monitor, sofa, table, toilet) and 200 images are prepared to an object class. Each image has different object identities, which means it rendered from different object 3D models (Princeton ModelNet, a 3D CAD model dataset for computer vision and cognitive science [https://modelnet.cs.princeton.edu/]). To render image of object dataset, horizontal viewpoint variation angle was randomly set between -30° and +30°. In object dataset, brightness and contrast of images are statistically comparable across the object class.<br> <strong>- Viewpoint dataset for invariance test (Folder name: invariance_test)</strong>: This set was used to test the viewpoint invariant characteristic of object selective units. This dataset consists of 13 subsets which has different viewpoints from -180° to +180° in linear scale step. It contains 200 different object identities in an object class, which are the same as those used in the object dataset.<br> <strong>- Viewpoint dataset for finding invariant unit (Folder name: invariance_unit)</strong>: This set was used to find object selective units that specifically or invariantly responded to object images of different viewpoints. This dataset consists of five angle-based viewpoint classes (-60°, -30°, 0°, 30°, 60°) with 50 object identities which were not used to find object selective unit<br> <strong>- SVM dataset (Folder name: SVM_var)</strong>: This set was used to train and test SVM which performs object detection task. It contains 60 different object identities in an object class, which were not used to find object selective unit. Specifically, it consists of 18 subsets which has different viewpoint variation range from 0° to 180°. For example, subset with 180° viewpoint variation range contains images which shows different viewpoints of objects within range of -90° and +90°.</p>
Identifying mismatches between conservation area networks and vulnerable populations using spatial randomization
<p>Grassland birds are among the most globally threatened bird groups due to substantial degradation of native grassland habitats. However, the current network of grassland conservation areas may not be adequate for halting population declines and biodiversity loss. Here, we evaluate a network of grassland conservation areas within Wisconsin, U.S.A. that includes both large Focal Landscapes and smaller targeted conservation areas (e.g., Grassland Bird Conservation Areas or GBCAs) established within them. To date, this conservation network has lacked baseline information to assess whether the current placement of these conservation areas aligns with population hotspots of grassland-dependent taxa. To do so, we fitted data from thousands of avian point-count surveys collected by citizen scientists as part of Wisconsin's Breeding Bird Atlas II with multinomial N-mixture models to estimate habitat-abundance relationships, develop spatially-explicit predictions of abundance and establish ecological baselines within priority conservation areas for a suite of obligate grassland songbirds. Next, we developed spatial randomization tests to evaluate the placement of this conservation network relative to randomly placed conservation networks. Overall, less than 20% of species statewide populations were found within the current grassland conservation network. Spatial tests demonstrated high representation of this bird assemblage within the entire conservation network, but with a bias towards birds associated with moderately tall grasses relative to those associated with short or tall grasses. We also found that GBCAs had higher representation at Focal Landscape rather than statewide scales. Here, we demonstrated how combining citizen science data with hierarchical modeling is a powerful tool for estimating ecological baselines and conducting large-scale evaluations of an existing conservation network for multiple grassland birds. Our flexible spatial randomization approach offers the potential to be applied to other protected area networks and serve as a complementary tool for conservation planning efforts globally.</p>
Displacement measurement via self mixing interferometry and neural network training set
<p>Self mixing interferometry is a simple and robust sensing method which can be used (among other things) to measure the displacement of a target along the light propagation axis. While conceptually simple, the actual use of this method is less straightforward than originally envisioned because reconstructing the target displacement from the interferometric signal is often tricky. A small neural network can do this task very well after proper training, as described in [10.1364/OE.419844]. This data set was used to train the network in that work (after data augmentation). It consists of a python dictionary with two keys: `truth` and `signal`. The `truth` part is a 195011-elements long numpy array corresponding to the displacement of the target in units of wavelength per 1.024 ms. The `signal` part is the interferometric signal corresponding to the displacement. It is arranged in a (195011,256,1) numpy array. Each segment of length 256 corresponds to the interferometric signal acquired during a 1.024 ms time window. For instance, the displacement value in `truth[618]` corresponds to the interferometric signal segment `signal[618,:,0]`.</p>
Vesuvio cGPS network – RINEX data quality control (2001 – 2019)
<p>For each cGPS station, the file contains a summary report with information about Rinex observation data. For each day, the summary line shows the following information:</p> <ol> <li>cGPS station name (Name),</li> <li>the start time of the window, the time format is year (Y), month (M), day (D), hour and minutes (Hour), day of year (DOY), modified Julian date (M J Date) and GPS week (Week),</li> <li>the end time of the window, the time format is year (Y), month (M), day (D), hour and minutes (Hour), day of year (DOY), modified Julian date (M J Date), and GPS week (Week),</li> <li>the start and end times of the window (time format is year month day hour min),</li> <li>window time laps (Hrs),</li> <li>observation interval (OI),</li> <li>the number of possible observations (#expt) above the elevation mask,</li> <li>the number of complete observations (#obs),</li> <li>the ratio of complete to possible observations as a percent (DCP),</li> <li>the RMS “multipath combinations” values MP1 and MP2, in meters, limited by the elevation mask (MP1, MP22) rounded to two decimal points,</li> <li>cycle slips (CS),</li> <li>the ratio of complete observations to cycle slips (obs/CS).</li> </ol> <p>A full description of cGPS network is reported in:<br> - De Martino P, Dolce M, Brandi G, Scarpato G, Tammaro U (2021). The Ground Deformation History of the Neapolitan Volcanic Area (Campi Flegrei Caldera, Somma–Vesuvius Volcano, and Ischia Island) from 20 Years of Continuous GPS Observations (2000–2019). Remote Sensing. 13(14):2725. doi:10.3390/rs13142725.</p> <p>Please cite this when using the dataset</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.